Abstract: Systems and methods are for routing electronic payment transactions to PIN-less networks using payment pseudo-networks and electronic transaction simulation.

Abstract: In some examples, sensor data comprising a first set and second set of sensor data can be received. The first set of sensor data can be provided to a rule-based classifier to identify a first normalized data tag. The second set of sensor data can be provided to a trained classifier to identify a second normalized data tag. The trained classifier can include a machine-learning model that has been trained based on tag sensor training data for classifying the second set of sensor data into a respective class associated with the second normalized data tag. The first and second sets of sensor data can be updated with respective first and second normalized data tags to normalize the first and second sets of sensor data to provide a standardized data label for each of the first and second sets of sensor data.

Abstract: A method for authentication of coupons using a blockchain includes: storing transaction data for a payment transaction, wherein the transaction data includes at least a transaction amount; receiving an identification value; receiving a block included in a blockchain, wherein the block includes at least a block header and a plurality of transaction values, each transaction value including at least a coupon identifier and coupon data; executing a query on the received block to identify a specific transaction value of the plurality of transaction values where the included coupon identifier corresponds to the received identification value; and executing a query on the memory to update at least the transaction amount included in the stored transaction data based on the coupon data included in the identified specific transaction value.

Abstract: A method and system for providing data imbalance detection and validation for a trained a ML model includes receiving a request to perform data imbalance detection on the trained ML model, identifying a feature of a dataset associated with the trained model for which data imbalance detection is to be performed, receiving access to the dataset, receiving access to the trained ML model, examining at least one of the dataset or outcome data generated by the trained ML model to determine a distribution of the feature or a distribution of the outcome data, and determining if the trained ML model exhibits data imbalance based at least in part on the distribution of the feature or the distribution of the outcome data.

Abstract: Systems and methods are provided to monitor performance of a machine learning model, the method may include steps of: receiving or storing one or more model data sets representative of the machine learning model, wherein the machine learning model has being trained with a first set of training data; analyzing the first set of training data based on one or more performance parameters for the machine learning model, to generate one or more performance data sets; and process the one or more performance data sets to determine one or more values representing a performance of the machine learning model.

Abstract: Apparatus and associated methods relate to a rewarded viewing video distribution system configured to reward a user for viewed video content based on presenting video content to a user, assigning the user rewards generated based on the value of the video portion viewed, and automatically presenting the user with marketplace credit to spend the rewards. In some embodiments, the viewed video portion value may be determined as a function of a predetermined reward threshold. In an illustrative example, the predetermined reward threshold may be the video percentage viewed. Various implementations may employ a reward threshold based on advertisements viewed, permitting advertisement spending adapted as a function of verified advertisement views. Some examples may reward views using a video player plug-in. Some embodiments may permit a user to spend viewing rewards on affiliated partner services or products.

Abstract: A method and system for detecting imbalanced distribution of data that may signal bias in a dataset associated with training a machine-learning (ML) model includes receiving a request to perform data imbalance detection on the dataset associated with training the machine-learning (ML) model, identifying a feature of the dataset for which data imbalance detection is to be performed and examining the dataset to determine a distribution of the feature across the dataset. The result of the determination may then be presented in a user interface element to help identify data imbalance in the dataset.

Abstract: Embodiments of methods and apparatuses for acquiring information are provided. One implementation of the method can include: determining an initial area corresponding to a target point of interest, and dividing the initial area corresponding to the target point of interest into a plurality of grids; and generating annotations of the plurality of grids respectively based on characteristics of the plurality of grids. Therefore, whether the user data associated with the each of the plurality of grids is used to generate the attribute of the target point of interest may be directly determined based on the annotation of the each of the plurality of grids, thereby saving the spending of the process of acquiring the user data required for generating the attribute of the target point of interest in data mining. The operation state of the target point of interest may be determined based on the annotation of the grid.

Abstract: Provided are an electronic device and a method for controlling same. The present method for controlling the electronic device may allow for on the basis of an image obtained by a camera of the electronic device, acquiring information associated with the user, identifying at least one advertisement category among a plurality of advertisement categories; when an event for displaying an advertisement occurs, transmitting information on the at least one advertisement category to an advertisement platform server; and receiving an advertisement in the at least one advertisement category from the advertisement platform server.

Abstract: Methods, apparatus, systems and articles of manufacture to estimate and deduplicate audiences are disclosed herein. An example apparatus includes a controller to determine a subunion of at least first and second marginal audiences of media based on of panel data and census data, the panel data including a panel impression count and a panel audience size, and the census data including a census impression count, an audience size calculator to determine a census audience size of the at least the first and second marginal audiences based on the panel impression count and the panel audience size and determine a subunion census audience size, the subunion census audience size corresponding to an overlap between the at least the first and second marginal audiences; and a report generator to generate a report including the census audience size and the subunion census audience size.

Abstract: A method of utilizing tones includes receiving content including at least an audio portion for broadcast to a first plurality of users having a first recipient computing device and a second plurality of users having a second recipient computing device, receiving data comprising a first and a second message, determining one or more locations within the content for embedding the first and second messages, converting at least the first and second messages into a plurality of tones that cannot be directly detected by the first and second pluralities of users during broadcast, incorporating the plurality of tones into the content at the one or more locations, and providing the content with the plurality of tones for broadcast via a presentation device such that the plurality of tones are detected by the first and second recipient devices for determining at least one of the first and second messages.

Abstract: Embodiments of the present systems and methods may provide improved machine learning performance even though data drift has occurred. For example, a method may comprise providing a machine learning model in a computer system, operating the machine learning model using a first dataset to obtain results of the first dataset, operating the machine learning model using a second dataset to obtain results of the second dataset, performing statistical testing on a confidence distribution of results of the first dataset and of results of the second dataset to determine a difference in a result confidence distribution between the first dataset and of the second dataset, and determining whether data included in the second dataset has data drift relative to the first dataset based on the difference in a result confidence distribution between the first dataset and of the second dataset.

Abstract: A processing system for fuel transactions analyzes transactions for symbols indicating a purchase amount and directs information about the purchase to a loyalty platform. The loyalty platform may identify and communicate in real time or near real time with a personal device associated with the purchaser of the fuel. A user interface of the personal device may allow the purchaser to review and select options for processing the fuel purchase using value from a loyalty program account. The selected option may be used to modify fuel purchase parameters prior to settlement of the transaction or simply add points to the user loyalty program account.

Abstract: A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.

Abstract: There is disclosed a method system for selecting a label for a task, the method comprising: receiving a plurality of labels, each of the label included within the plurality of labels being indicative of a given assessor's perceived preference of a first object of over a second object; analyzing the comparison task to determine a set of latent biasing features; executing a MLA configured to generating a respective latent score parameter for the first object and the second object, the respective latent score parameter indicative of a probable offset between the given assessor's perceived preference and an unbiased preference parameter of the first object over the second object; generating a predicted bias degree parameter for the given assessor; generating the unbiased preference parameter; using, by the server, the unbiased preference parameter as the label for the comparison task for the given assessor.

Abstract: Embodiments are disclosed for a method that may include accessing, from a first data source, events including interactions between a mobile device and one or more network devices on a network at a locale, and receiving external data from a second data source that excludes the network devices. The method may further include determining, based on the interactions, one or more geographic positions and one or more corresponding time intervals of the mobile device at the geographic positions, and correlating the geographic positions and the time intervals with the external data to obtain a metric.

Abstract: Techniques for improving packaging systems are described. In an example, a computer system receives item data from a workstation. The item data includes a description of an item. The computer system also receives fit parameters. The fit parameters adjust the fit of items within packaging. The workstation is configured to facilitate packaging of the item. Based at least in part on an input to a packaging decision system, the computer system generates a package decision indicating a package type associated with the packaging of the item. The input is based at least in part on the item data. The packaging decision system is trained based at least in part on packaging data associated with historical item orders. The computer system sends the package decision to the workstation.

Abstract: An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.

Abstract: An identity recognition system based on compressed signals and a method thereof are provided. When a sensing end is in an identification mode, it continuously measures a physiological identification signal of a user having at least one first predetermined length of time, and performs a compression process on the physiological identification signal having at least one first predetermined length of time to generate a first compressed signal. An identification end receives the first compressed signal, obtains first identification information of the first compressed signal in a discrimination subspace according to a principal eigenvector, and recognizes the first identification signal according to a classification model from a learning end to determine that the user is one of multiple subjects.

Abstract: A system for training a classification model to be robust against perturbations of multiple perturbation types. A perturbation type defines a set of allowed perturbations. The classification model is trained by, in an outer iteration, selecting a set of training instances of a training dataset; selecting, among perturbations allowed by the multiple perturbation types, one or more perturbations for perturbing the selected training instances to maximize a loss function; and updating the set of parameters of the classification model to decrease the loss for the perturbed instances. A perturbation is determined by, in an inner iteration, determining updated perturbations allowed by respective perturbation types of the multiple perturbation types and selecting an updated perturbation that most increases the loss of the classification model.